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2021 CIE International Conference on Radar, Radar 2021

作者： Di, Jiang Xinwu, Li Chen, Gong Wen, Hong Yirong, Wu Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China

ISBN: (纸本)9781665498142

Supraglacial lake plays an important role in ice sheet dynamics, mass balance and sea level rise. Therefore, it is of great importance to extract supraglacial lake and obtain its spatial-temporal distribution or change. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) imagery based on deep learning. First, select 19580 Sentinel-1 SAR imagery patches in eight typical areas for manual labeling. Second, the GPU-based U-Net model is used to implement the training of the supraglacial lake, and the results are evaluated in different sites. Finally, the training model is used to perform the supraglacial lake extraction. In addition, this article also introduces ArcticDEM to remove shadow confusion in the margin of the ice sheet. The global-local threshold segmentation method is used to extract the supraglacial lake on the Sentinel-2 MSI imagery, which is a comparative analysis and information supplement for the extracted results in this paper. The results show that: (1) The U-Net network selected in this paper is suitable for processing small sample size SAR data and multi-modal feature extraction. The GPU parallel processing method can achieve rapid extraction of massive data and reduce time cost. (2) The Dice coefficient of the training model reaches 0.98, which can be used for effective extraction of supraglacial lake. (3) Compared with the results of optical image extraction, the algorithm proposed in this paper can identify lakes in areas covered by snow or thin ice, which truly reflects the supraglacial lake temporal and spatial distribution characteristics. © 2021 IEEE.

关键词： Greenland SAR Supraglacial lake U-Net

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A LINEARLY CONVERGENT METHOD FOR SOLVING HIGH-ORDER PROXIMAL OPERATOR∗

arXiv

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arXiv 2023年

作者： Jingyu, G.A.O. Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

Recently, various high-order methods have been developed to solve the convex optimization problem. The auxiliary problem of these methods shares the general form that is the same as the high-order proximal operator proposed by Nesterov. In this paper, we present a linearly convergent method to solve the high-order proximal operator based on the classical proximal operator. In addition, some experiments are performed to demonstrate the performance of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

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UNSUPERVISED STEREO MATCHING NETWORK FOR VHR REMOTE SENSING IMAGES BASED ON ERROR PREDICTION

arXiv

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arXiv 2024年

作者： Jiang, Liting Xiang, Yuming Wang, Feng You, Hongjian Aerospace Information Research Institute Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

Stereo matching in remote sensing has recently garnered increased attention, primarily focusing on supervised learning. However, datasets with ground truth generated by expensive airbone Lidar exhibit limited quantity and diversity, constraining the effectiveness of supervised networks. In contrast, unsupervised learning methods can leverage the increasing availability of very-high-resolution (VHR) remote sensing images, offering considerable potential in the realm of stereo matching. Motivated by this intuition, we propose a novel unsupervised stereo matching network for VHR remote sensing images. A light-weight module to bridge confidence with predicted error is introduced to refine the core model. Robust unsupervised losses are formulated to enhance network convergence. The experimental results on US3D and WHU-Stereo datasets demonstrate that the proposed network achieves superior accuracy compared to other unsupervised networks and exhibits better generalization capabilities than supervised models. Our code will be available at https://***/Elenairene/CBEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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HIGH-ORDER PROXIMAL POINT ALGORITHM FOR THE MONOTONE VARIATIONAL INEQUALITY PROBLEM AND ITS application

arXiv

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arXiv 2023年

作者： Gao, Jingyu Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

The proximal point algorithm (PPA) has been developed to solve the monotone variational inequality problem. It provides a theoretical foundation for some methods, such as the augmented Lagrangian method (ALM) and the alternating direction method of multipliers (ADMM). This paper generalizes the PPA to the pth-order (p ≥ 1) and proves its convergence rate O(1/kp/2). Additionally, the pth-order ALM is proposed based on the pth-order PPA. Some numerical experiments are presented to demonstrate the performance of the pth-order ALM. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

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SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

arXiv

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arXiv 2023年

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong The Key Laboratory of Technology in Geo-spatial Information Processing Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine nonconvex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network’s parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Deep amended COPERT model for regional vehicle emission prediction

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Science China(information Sciences) 2021年第3期64卷 238-240页

作者： Zhenyi XU Yu KANG Yang CAO Zerui LI Department of Automation University of Science and Technology of China Key Laboratory of Technology in Geo Spatial Information Processing and Application System Chinese Academy of Sciences

Dear editor,The existing methods for regional vehicle emission prediction can be roughly categorized into the classes of classical dispersion models and satellite remote sensing *** plume models, operational street ca...

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Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

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2021 CIE International Conference on Radar, Radar 2021

作者： Du, Bang Zhang, Zhe Qiu, Xiaolan Lei, Bin Ding, Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Beijing China University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9781665498142

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named 'Multi-aspect TomoSAR,' which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later. © 2021 IEEE.

关键词： Distributed compressed sensing Joint sparsity model Multi-aspect TomoSAR

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

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2022 International Conference on Radar systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

arXiv

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arXiv 2024年

作者： Gao, Silin Wang, Wenlong Wang, Muhan Zhang, Zhe Yang, Zai Qiu, Xiaolan Zhang, Bingchen Wu, Yirong Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics & Statistics Xi’an Jiaotong University Xi’an710049 China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Suzhou215123 China China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on 1 norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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